Wednesday, 24 February 2016

This post builds on the basis of a previous post on safepoints. If you've not read it you might feel lost and confused. If you have read it, and still feel lost and confused, and you are certain this feeling is related to the matter at hand (as opposed to an existential crisis), please ask away.
So, now that we've established what safepoints are, and that:

We have all the information we need to conclude that profiling by sampling at a safepoint is perhaps a bit shite. This will not come as a surprise to some, but this issue is present in the most commonly used profilers. According to this survey by RebelLabs this is the breakdown:

VisualVM, NB Profiler(same thing), YourKit and JProfiler all provide a sampling CPU profiler which samples at a safepoint. Seeing how this is a rather common issue, lets dig into it.

How Sampling Execution Profilers Work (in theory)

Sampling profilers are supposed to approximate the 'time spent' distribution in our application by collecting samples of where our application is at different points in time. The data collected at each sample could be:

current instruction

current line of code

current method

current stack trace

The data can be collected for a single thread or all threads at each sample. What do we need to hold for sampling to work?

"However, for sampling
to produce results that are comparable to a full (unsampled) profile,
the following two conditions must hold.
First, we must have a large number of samples to get statistically
significant results. For example, if a profiler collects only a single
sample in the entire program run, the profiler will assign 100% of
the program execution time to the code in which it took its sample
and 0% to everything else. [...]Second, the profiler should sample all points in a program run
with equal probability. If a profiler does not do so, it will end up with bias in its profile. For example, let’s suppose our profiler can
only sample methods that contain calls. This profiler will attribute
no execution time to methods that do not contain calls even though
they may account for much of the program’s execution time." - from Evaluating the Accuracy of Java Profilers, we'll get back to this article in a bit

That sounds easy enough, right?

Once we have lots of samples we can construct a list of hot methods, or even code lines in those methods (if the samples report it), we can look at the samples distributed on the call tree (if call traces are collected) and have an awesome time!

How Do Generic Commercial Java Sampling Execution Profilers Work?

Well, I can sort of reverse engineer here from different solutions, or read through open source code bases, but instead I'll offer unsupported conjecture and you are free to call me out on it if you know better. Generic profilers rely on the JVMTI spec, which all JVMs must meet:

JVMTI offers only safepoint sampling stack trace collection options (GetStackTrace for the calling thread doesn't require a safepoint, but that is not very useful to a profiler. On Zing GetStackTrace to another thread will bring only that thread to a safepoint.). It follows that vendors who want their tools to work on ALL JVMs are limited to safepoint sampling.

You hit a global safepoint whether you are sampling a single thread or all threads (at least on OpenJDK, Zing is slightly different but as a profiler vendor OpenJDK is your assumption.). All profilers I looked into go for sampling all threads. AFAIK they also do not limit the depth of the stack collected. This amounts to the following JVMTI call: JvmtiEnv::GetAllStackTraces(0, &stack_info, &thread_count)

So this adds up to: setup a timer thread which triggers at 'sampling_interval' and gathers all stack traces.

This is bad for several reasons, some of which are avoidable:

Sampling profilers need samples, so it is common to set sampling frequency is quite high (usually 10 times a second, or every 100ms). It's instructive to set the -XX:+PrintGCApplicationStoppedTime and see what sort of pause time this introduces. It's not unusual to see a few milliseconds pause, but YMMV(depending on number of threads, stack depth, TTSP etc). A 5ms pause every 100ms will mean a 5% overhead (actual damage is likely worse than that) introduced by your profiler. You can usually control the damage here by setting the interval longer, but this also means you will need a longer profiling period to get a meaningful sample count.

Gathering full stack traces from all the threads means your safepoint operation cost is open ended. The more threads your application has (think application server, SEDA architecture, lots of thread pools...), and the deeper your stack traces (think Spring and Co.) the longer your application will wait for a single thread to go round taking names and filling forms. This was clearly demonstrated in the previous post. AFAIK, current profilers do nothing to help you here. If you are building your own profiler it would seem sensible to set a limit on either quantities so that you can box your overheads. The JVMTI functionality allows you to query the list of current threads, you could sample all if there's less than a 100 and otherwise pick a random subset of 100 to sample. It would make sense to perhaps bias towards sampling threads that are actually doing something as opposed to threads which spend all their time blocked.

As if all that was not bad enough, sampling at a safepoint is a tad meaningless.

Points 1 and 2 are about profiling overheads, which is basically about cost. In my previous post on safepoints I looked at these costs, so there's no point repeating the exercise. Cost may be acceptable for good profiling information, but as we'll see the information is not that great.
Point 3 bears explaining, so off we go in the search for meaning.

Safepoint Sampling: Theory

So what does sampling at a safepoint mean? It means only the safepoint polls in the running code are visible. Given hot code is likely compiled by C1/C2 (client/server compilers) we have reduced our sampling opportunities to method exit and uncounted loop backedges. This leads to the phenomena called safepoint bias whereby the sampling profiler samples are biased towards the next available safepoint poll location (this fails the second criteria set out above "the profiler should sample all points in a program run with equal probability").
This may not sound so bad at first, so lets work through a simple example and see which line gets the blame.NOTE: In all of the following examples I will be using JMH as the test harness and make use of the 'CompilerControl' annotation to prevent inlining. This will let me control the compilation unit limits, and may seem cruel and unusual, or at least unfair, of me. Inlining decisions in the 'wild' are governed by many factors and it is safe (in my opinion) to consider them arbitrary (in the hands of several compilers/JVM vendors/command line arguments etc.). Inlinining may well be the "mother of all optimizations", but it is a fickle and wily mother at that.
Let's look at something simple:

This is an easy example to think about. We can control the amount of work in the method by changing the size of the array. We know the counted loop has no safepoint poll in it (verified by looking at the assembly output), so in theory the methods above will have a safepoint at method exit. The thing is, if we let the method above get inlined the end of method safepoint poll will disappear, and the next poll will be in the measurement loop:

So, it would seem reasonable to expect the method to get blamed if it is not inlined, but if it does get inlined we can expect the measurement method to get blamed. Right? very reasonable, but a bit off.

Safepoint Sampling: Reality

Safepoint bias is discussed in this nice paper from 2010: Evaluating the Accuracy of Java Profilers, where the writers recognize that different Java profilers identify different hotspots in the same benchmarks and go digging for reasons. What they don't do is set up some benchmarks where the hotspot is known and use those to understand what it is that safepoint biased profilers see. They state:

"If we knew the “correct” profile for a program run, we could evaluate
the profiler with respect to this correct profile. Unfortunately,
there is no “correct” profile most of the time and thus we cannot
definitively determine if a profiler is producing correct results."

So, if we construct a known workload... what do these profilers see?

We'll study that with the JMH safepoint biased profiler "-prof stack". It is much like JVisualVM in the profiles it presents for the same code, and a damn sight more convenient for this study.NOTE: In the following sections I'm using the term sub-method to describe a method which is being called into from another method. E.g. if method A calls method B, B is a sub method of A. Maybe a better terminology exists, but that's what I mean here.

If we run the samples above we get 2 different hot lines of code (run with '-prof stack:detailLine=true'):# Benchmark: safepoint.profiling.SafepointProfiling.meSoHotInline

....[Thread state: RUNNABLE]...

99.6% 99.8% meSoHotInline_avgt_jmhStub:165

# Benchmark: safepoint.profiling.SafepointProfiling.meSoHotNoInline

....[Thread state: RUNNABLE]...

99.4% 99.6% meSoHotNoInline_avgt_jmhStub:163

Neither one in the actually hot method. It seems that the method exit safepoint is not deemed indicative of it's own method, but rather of the line of code from which it was called. So forcing the method under measurement to not inline implicated the calling line of code in the measurement loop, while leaving it to inline meant the back edge of the loop got the blame. It also appears that an uncounted loop safepoint poll is deemed indicative of it's own method.

We may deduce(but we won't necessarily be correct) that when looking at this kind of profile without line of code data a hot method is indicative of:

Having line of code data can help disambiguate the above cases, but it's not really very useful as line of code data. A hot line of code would be indicative of:

Line has a method call: A method called from this line is hot (or maybe it's inlined sub-methods are)*

Line is a loop back edge: Some code (include inlined submethods) in this loop is hot*

*Does that seem useful? Don't get your hopes up.
Because we usually have no idea which methods got inlined this can be a bit confusing (you can use -XX:+PrintInlining if you want to find out, but be aware that inlining decisions can change from run to run).

Mind The Gap

If the above rules held true you could use a safepoint biased profile by examining the code under the blamed node in the execution tree. In other words, it would mean a hot method indicates the hot code lies somewhere in that code or in the method it calls. This would be good to know, and the profile could serve as a starting point for some productive digging. But sadly, these rules do not always hold true. They are missing the fact that the hot code can be anywhere between the indicated safepoint poll and the previous one. Consider the following example:

Clearly, the time is spent in the loop before calling setResult, but the profile blames setResult. There's nothing wrong with setResult except that a method it calls into is not inlined, providing our profiler with a blame opportunity. This demonstrates the randomness with which the safepoint poll opportunities present themselves to user code, and shows that the hot code may be anywhere between the current safepoint poll and the previous one. This means that a hot method/line-of-code in a safepoint biased profile are potentially misleading without some idea of where the previous safepoint poll is. Consider the following example:

The profiler implicates the caller to a cheap method 9 levels down the stack, but the real culprit is the loop at the topmost method. Note that inlining will prevent a method from showing, but not-inlined frames only break the gap between safepoints on return (on OpenJDK anyhow. This is vendor and whim dependent. e.g. Zing puts the method level safepoint on entry for instance and I'm not sure where J9 stands on this issue. This is not to say that one way is somehow better than the other, just that the location is arbitrary). This is why setResult6 which is not inlined and higher up the stack doesn't show up.

Summary: What Is It Good For?

As demonstrated above, a safepoint sampling profiler can have a wildly inaccurate idea of where the hot code in your application is. This renders derived observations on "Running" threads pretty suspect, but they are at least correct observations on which threads are running. This doesn't mean they are entirely useless and sometimes all we need is a hint in the right direction for some good analysis to happen, but there's a potential for huge waste of time here. While samples of code running in the interpreter do not suffer safepoint bias, this is not very useful as hot code is quickly compiled. If your hot code is running in the interpreter you have bigger fish to fry than safepoint bias...

The stack traces for blocked threads are accurate, and so the "Waiting" profile is useful to discover blocked code profile. If blocking methods are at the root of your performance issue than this will be a handy observation.

There are better options out there! I'll get into some of them in following posts:

No tool is perfect, but all of the above are a damn sight better at identifying where CPU time is being spent.
Big thanks to the many reviewers, if you find any issues please comment and I will punish them severely.

Monday, 1 February 2016

Will get your JVM to hang and will not exit after 5 seconds, as one might expect from reading the code. The reason this happens, I claimed, is because the compiler considers for loops limited by an int to be counted, and will therefore not insert a safepoint poll into the generated assembly code. This was the theory, and I tested it, and it definitely hung on my machine ('It Works On My Machine'™).
To my great surprise, some people not only read the posts, they also run the code (it's like they don't trust me ;-) ). And 2 of those diligent readers got back to me in the comments saying that actually the JVM exists just fine. So I ran it again, and it hung, again. Then I ran it with different versions of the JVM, and it hung as I expected. Then I thought, maybe they are running the code I put on GitHub, so I tried that. That also hung when I ran it from Eclipse, but did not when I built it with Maven and ran it from the command line. EUREKA!

As it happens, Eclipse has it's own Java compiler (a fact well known to those who know it well, no doubt) and the bytecode it generated (the .class file) hung every time, but Maven uses javac and the .class file generated from that did not hang. When testing initially I was running my class inside Eclipse, or using the Eclipse .class file to run from the command line. Both .class files are valid, but different (same-same but different). We can use javap to print out class bytecode and compare ("javap -c -v -p <class file>"). For your pleasure you can find the original Java file, the 2 class files and their respective bytecode printouts on GitHub and come to your own conclusions before you continue reading.
So... is my understanding (and the Eclipse javac) somehow out of date? can this problem of telling counted loops from uncounted loops be solved by the bytecode compiler?

How is the bytecode different?

Well, mostly not that different:

An easy to spot difference which we can tell is semantically the same is the bytecode result for "System.out.println("How Odd:" + l);" you'll notice that Eclipse is using the constructor "java/lang/StringBuilder."<init>":(Ljava/lang/String;)" while javac is opting for "java/lang/StringBuilder."<init>":()V" followed by an append. Semantically the 2 choices are the same. We create a StringBuilder and append the String "How odd:" and the variable l. The reality is that there are subtle differences, the Eclipse version will allocate a larger char[] for instance. It may also be that the Eclipse pattern will miss out on some String concatenation optimization because the OpenJDK implementers are likely to focus on patterns generated from the official javac.

The loop interpretation is different. Eclipse is checking the loop conditions at the end of the loop whereas javac is building a loop with the checks upfront and the loop backedge at the end. That seems like the important difference.

Let's zoom in on the relevant bytecode for the loop:

If we were to reverse translate the loop bytecode to ugly Java (which supported goto) we'd get:

Note that there's no special Java bytecode for loop control flows. So why so different at runtime? If we dump out the assembly for the run method we will see that the Eclipse version is missing the safepoint in the loop (the method has a {poll_return} but there's no {poll}), and the javac version has that safepoint there. Is it the bytecode difference that makes this happen?

Variations on bytecode

I tinkered with the class a bit and refactored the loop out of the lambda. This results in similar bytecode for the loop and same behaviour. I then made the limit of the loop a parameter of the method, this time it behaved like the javac version, but had the same funky loop structure:

WTF? if this is a purely a bytecode subtlety then it's seems pretty weird. What sort of flaky compiler would swing like this?

The compiler did it?

But... which compiler? so we looked at the above bytecode compiler results, this is but the first of many compilers to process your code on it's way to being executed. Traditionally when discussing Java code compilation we look at the following stages:

Javac: From code to bytecode

Interpreter: Step through the bytecode

C1/Client: Short compilation time, does not generate the best optimized code. Intended for short lived applications.

And the compilation timeline would look something like this: /->C1*.java->javac->*.class->interpreter->class+profile< \->C2The situation with OpenJDK8 is more complex with Tiered Compilation allowing us a smooth transition from interpreter to C2 compilation via C1 compilations. Given the behaviour we are after was supposed to manifest after a C1/C2 compilation we can run the code above to find which compiler compiled the method in question by adding the flag: -XX:+PrintCompilation: 159 201 % 3 safepoint.hang.WhenWillItExitInt2::countOdds @ 13 (44 bytes) 159 202 3 safepoint.hang.WhenWillItExitInt2::countOdds (44 bytes) 159 203 % 4 safepoint.hang.WhenWillItExitInt2::countOdds @ 13 (44 bytes) 162 201 % 3 safepoint.hang.WhenWillItExitInt2::countOdds @ -2 (44 bytes) made not entrant 142 200 % 3 safepoint.hang.WhenWillItExitInt2::countOdds @ -2 (44 bytes) made not entrant 1896 202 % 4 safepoint.hang.WhenWillItExitInt2::countOdds @ -2 (44 bytes) made not entrant 1896 203 % 3 safepoint.hang.WhenWillItExitInt2::countOdds @ 13 (44 bytes) 1896 204 % 4 safepoint.hang.WhenWillItExitInt2::countOdds @ 13 (44 bytes) 1899 203 % 3 safepoint.hang.WhenWillItExitInt2::countOdds @ -2 (44 bytes) made not entrantThe 3/4 printouts on the 4th column above indicate the method is compiled by C1 (profiled) and C2. But what is that % about? This is an indicator that the compilations are not 'normal' compilations but rather On Stack Replacement(OSR) compilations. OSR compilations are 'special', from the OpenJDK glossary:

on-stack replacement
Also known as 'OSR'. The process of converting an interpreted (or less optimized) stack frame into a compiled (or more optimized) stack frame. This happens when the interpreter discovers that a method is looping, requests the compiler to generate a special nmethod with an entry point somewhere in the loop (specifically, at a backward branch), and transfers control to that nmethod. A rough inverse to deoptimization.

I like the "rough inverse to deoptimization", it's a great mental qualification helper. OSR is the opposite of deoptimization because it is a mid-method replacement of code with compiled code, whereas a deoptimization is a mid-method replacement of code with interpreted code. Normal compilation unit replacements happen out of method, so while the method is off stack. OSR is specifically done to replace long running loops, like our countOdds method. As we can see from the printout above both C1 and C2 can produce OSR compilations.
It is impossible to disable OSR directly in a product build (there's a developer option -XX:-CICompileOSR), but we can force upfront compilation by using the following flags: "-XX:-TieredCompilation -server/client -Xcomp". Running with these arguments all 3 variations (original, refactored loop, refactored parametrized loop) now hang both for the Eclipse and the javac generated bytecode.
In both cases where the Eclipse versions hang we can see OSR compilations of the relevant code.
So it seems:

C1/C2 non-OSR compilations of the loop treat it as a counted loop, thus eliminating the safepoint poll and demonstrating the hung behaviour due to huge TTSP.

C1/C2 OSR compilations of the same code result in different results depending on the generated bytecode for the loop. I'm not going to dig further into the reasons, but I would welcome any feedback from OpenJDK compiler guys.

The OSR compilation decision to treat the loop as non-counted is quite reasonable. If we have a loop which remains on stack for very long periods we should perhaps consider it uncounted.

Killer Task

I started down this path looking for valid Java code which will result in a hung JVM due to huge TTSP. Since OSR got in the way, can we build such a task? Sure we can, we just need to warmup the loop until it is C2 compiled and then run it with a large limit, like so:

This fucker now hangs in both the Eclipse and javac versions. Joy!

Side note: OSR and JMH

I look at benchmarks people write quite a bit, and it is very common to see people rolling their own benchmark harnesses put their "code under measurement" in a counted loop, run it 100,000 times and derive their results. Often that means the code they are measuring is OSR compiled code, which is different (for better or worse, mostly worse) from the code they will be running normally. If you are one of these people, I beg you: cut that fucking shit out!

JUST USE JMH!
PLEASE?

To a large extent it will cover your use case. Where it doesn't cover your use case, post a question on jmh-dev mailing list, it might be that you've missed something. If it turns out JMH cannot cover your usecase, make sure you've read the JMH samples before rolling your own harness.
I'm not going to include a before and after example, because it just makes me sad. Look at SO for some fine examples.

Counted Loops?

I keep saying 'counted loops' like it means something, so here are some examples for you to play with and contemplate:

So, as you can see the definition ain't so clear and I would imagine some of the above behaviour will vary depending on JVM version/vendor/phase of the moon etc.

Summary

So, it seems my previous example was not as tested as it should have been. I apologize to anyone who has based life changing decisions on that sample. I hope this post helps in explaining away the subtleties which led to my downfall.

The above examples help demonstrate that the line between counted and uncounted loops is a tad confusing and the classification may depend on differences in bytecode, compiler mode, and so on.